




"""simple docstring"""




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import DetrImageProcessor

class   UpperCAmelCase_    (			unittest.TestCase		):
		def __init__(						self				,						UpperCamelCase_				,						UpperCamelCase_=7				,						UpperCamelCase_=3				,						UpperCamelCase_=30				,						UpperCamelCase_=4_00				,						UpperCamelCase_=True				,						UpperCamelCase_=None				,						UpperCamelCase_=True				,						UpperCamelCase_=1 / 2_55				,						UpperCamelCase_=True				,						UpperCamelCase_=[0.5, 0.5, 0.5]				,						UpperCamelCase_=[0.5, 0.5, 0.5]				,						UpperCamelCase_=True				,						)						->       Optional[Any]:
					__lowercase			:  str								=       size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
					__lowercase			:  Any								=       parent
					__lowercase			:  List[Any]								=       batch_size
					__lowercase			:  List[str]								=       num_channels
					__lowercase			:  Optional[int]								=       min_resolution
					__lowercase			:  Any								=       max_resolution
					__lowercase			:  List[str]								=       do_resize
					__lowercase			:  List[str]								=       size
					__lowercase			:  Union[str, Any]								=       do_rescale
					__lowercase			:  Dict								=       rescale_factor
					__lowercase			:  int								=       do_normalize
					__lowercase			:  Optional[Any]								=       image_mean
					__lowercase			:  Dict								=       image_std
					__lowercase			:  Tuple								=       do_pad
		def 	_lowerCamelCase							(						self       )						->       Union[str, Any]:
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_rescale": self.do_rescale,
					    "rescale_factor": self.rescale_factor,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_pad": self.do_pad,
					}
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_=False       )						->       str:






					if not batched:
								__lowercase			:  Tuple								=       image_inputs[0]
								if isinstance(__UpperCamelCase				,						Image.Image       ):
											__lowercase			,__lowercase			:  Tuple								=       image.size
								else:
											__lowercase			,__lowercase			:  Union[str, Any]								=       image.shape[1], image.shape[2]
								if w < h:
											__lowercase			:  int								=       int(self.size['''shortest_edge'''] * h / w       )
											__lowercase			:  Optional[Any]								=       self.size['''shortest_edge''']
								elif w > h:
											__lowercase			:  str								=       self.size['''shortest_edge''']
											__lowercase			:  Optional[int]								=       int(self.size['''shortest_edge'''] * w / h       )
								else:
											__lowercase			:  Dict								=       self.size['''shortest_edge''']
											__lowercase			:  List[Any]								=       self.size['''shortest_edge''']

					else:
								__lowercase			:  int								=       []
								for image in image_inputs:
											__lowercase			,__lowercase			:  List[Any]								=       self.get_expected_values([image]       )
											expected_values.append((expected_height, expected_width)       )
								__lowercase			:  Union[str, Any]								=       max(__UpperCamelCase				,						key=lambda UpperCamelCase_       : item[0]       )[0]
								__lowercase			:  Optional[int]								=       max(__UpperCamelCase				,						key=lambda UpperCamelCase_       : item[1]       )[1]

					return expected_height, expected_width


@require_torch
@require_vision
class   UpperCAmelCase_    (			_UpperCAmelCase   ,					unittest.TestCase		):
		UpperCamelCase							=DetrImageProcessor if is_vision_available() else None
		def 	_lowerCamelCase							(						self       )						->       Tuple:
					__lowercase			:  str								=       DetrImageProcessingTester(self       )
		@property
		def 	_lowerCamelCase							(						self       )						->       Optional[int]:
					return self.image_processor_tester.prepare_image_processor_dict()
		def 	_lowerCamelCase							(						self       )						->       List[str]:
					__lowercase			:  Any								=       self.image_processing_class(**self.image_processor_dict       )
					self.assertTrue(hasattr(__UpperCamelCase				,						'''image_mean'''       )       )
					self.assertTrue(hasattr(__UpperCamelCase				,						'''image_std'''       )       )
					self.assertTrue(hasattr(__UpperCamelCase				,						'''do_normalize'''       )       )
					self.assertTrue(hasattr(__UpperCamelCase				,						'''do_rescale'''       )       )
					self.assertTrue(hasattr(__UpperCamelCase				,						'''rescale_factor'''       )       )
					self.assertTrue(hasattr(__UpperCamelCase				,						'''do_resize'''       )       )
					self.assertTrue(hasattr(__UpperCamelCase				,						'''size'''       )       )
					self.assertTrue(hasattr(__UpperCamelCase				,						'''do_pad'''       )       )
		def 	_lowerCamelCase							(						self       )						->       int:
					__lowercase			:  Union[str, Any]								=       self.image_processing_class.from_dict(self.image_processor_dict       )
					self.assertEqual(image_processor.size				,						{'''shortest_edge''': 18, '''longest_edge''': 13_33}       )
					self.assertEqual(image_processor.do_pad				,						__UpperCamelCase       )

					__lowercase			:  Optional[Any]								=       self.image_processing_class.from_dict(
					    self.image_processor_dict				,						size=42				,						max_size=84				,						pad_and_return_pixel_mask=__UpperCamelCase       )
					self.assertEqual(image_processor.size				,						{'''shortest_edge''': 42, '''longest_edge''': 84}       )
					self.assertEqual(image_processor.do_pad				,						__UpperCamelCase       )
		def 	_lowerCamelCase							(						self       )						->       Any:
					pass
		def 	_lowerCamelCase							(						self       )						->       Optional[Any]:
					__lowercase			:  Dict								=       self.image_processing_class(**self.image_processor_dict       )
					# create random PIL images
					__lowercase			:  Optional[Any]								=       prepare_image_inputs(self.image_processor_tester				,						equal_resolution=__UpperCamelCase       )
					for image in image_inputs:
								self.assertIsInstance(__UpperCamelCase				,						Image.Image       )

					# Test not batched input
					__lowercase			:  Dict								=       image_processing(image_inputs[0]				,						return_tensors='''pt'''       ).pixel_values

					__lowercase			,__lowercase			:  Union[str, Any]								=       self.image_processor_tester.get_expected_values(__UpperCamelCase       )

					self.assertEqual(
					    encoded_images.shape				,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,						)

					# Test batched
					__lowercase			,__lowercase			:  Union[str, Any]								=       self.image_processor_tester.get_expected_values(__UpperCamelCase				,						batched=__UpperCamelCase       )

					__lowercase			:  str								=       image_processing(__UpperCamelCase				,						return_tensors='''pt'''       ).pixel_values
					self.assertEqual(
					    encoded_images.shape				,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )				,						)
		def 	_lowerCamelCase							(						self       )						->       List[str]:
					__lowercase			:  Any								=       self.image_processing_class(**self.image_processor_dict       )
					# create random numpy tensors
					__lowercase			:  List[Any]								=       prepare_image_inputs(self.image_processor_tester				,						equal_resolution=__UpperCamelCase				,						numpify=__UpperCamelCase       )
					for image in image_inputs:
								self.assertIsInstance(__UpperCamelCase				,						np.ndarray       )

					# Test not batched input
					__lowercase			:  Any								=       image_processing(image_inputs[0]				,						return_tensors='''pt'''       ).pixel_values

					__lowercase			,__lowercase			:  Optional[int]								=       self.image_processor_tester.get_expected_values(__UpperCamelCase       )

					self.assertEqual(
					    encoded_images.shape				,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,						)

					# Test batched
					__lowercase			:  Optional[Any]								=       image_processing(__UpperCamelCase				,						return_tensors='''pt'''       ).pixel_values

					__lowercase			,__lowercase			:  List[Any]								=       self.image_processor_tester.get_expected_values(__UpperCamelCase				,						batched=__UpperCamelCase       )

					self.assertEqual(
					    encoded_images.shape				,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )				,						)
		def 	_lowerCamelCase							(						self       )						->       Any:
					__lowercase			:  List[Any]								=       self.image_processing_class(**self.image_processor_dict       )
					# create random PyTorch tensors
					__lowercase			:  Optional[Any]								=       prepare_image_inputs(self.image_processor_tester				,						equal_resolution=__UpperCamelCase				,						torchify=__UpperCamelCase       )
					for image in image_inputs:
								self.assertIsInstance(__UpperCamelCase				,						torch.Tensor       )

					# Test not batched input
					__lowercase			:  Union[str, Any]								=       image_processing(image_inputs[0]				,						return_tensors='''pt'''       ).pixel_values

					__lowercase			,__lowercase			:  List[Any]								=       self.image_processor_tester.get_expected_values(__UpperCamelCase       )

					self.assertEqual(
					    encoded_images.shape				,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,						)

					# Test batched
					__lowercase			:  Any								=       image_processing(__UpperCamelCase				,						return_tensors='''pt'''       ).pixel_values

					__lowercase			,__lowercase			:  Dict								=       self.image_processor_tester.get_expected_values(__UpperCamelCase				,						batched=__UpperCamelCase       )

					self.assertEqual(
					    encoded_images.shape				,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )				,						)
		@slow
		def 	_lowerCamelCase							(						self       )						->       Dict:
					__lowercase			:  str								=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
					with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''				,						'''r'''       ) as f:
								__lowercase			:  List[Any]								=       json.loads(f.read()       )

					__lowercase			:  Dict								=       {'''image_id''': 3_97_69, '''annotations''': target}

					# encode them
					__lowercase			:  Optional[Any]								=       DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''       )
					__lowercase			:  str								=       image_processing(images=__UpperCamelCase				,						annotations=__UpperCamelCase				,						return_tensors='''pt'''       )

					# verify pixel values
					__lowercase			:  Any								=       torch.Size([1, 3, 8_00, 10_66]       )
					self.assertEqual(encoding['''pixel_values'''].shape				,						__UpperCamelCase       )

					__lowercase			:  Union[str, Any]								=       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]       )
					self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,						__UpperCamelCase				,						atol=1E-4       )       )

					# verify area
					__lowercase			:  Dict								=       torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]       )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,						__UpperCamelCase       )       )
					# verify boxes
					__lowercase			:  str								=       torch.Size([6, 4]       )
					self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,						__UpperCamelCase       )
					__lowercase			:  Optional[int]								=       torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]       )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,						__UpperCamelCase				,						atol=1E-3       )       )
					# verify image_id
					__lowercase			:  Optional[int]								=       torch.tensor([3_97_69]       )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,						__UpperCamelCase       )       )
					# verify is_crowd
					__lowercase			:  Optional[Any]								=       torch.tensor([0, 0, 0, 0, 0, 0]       )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,						__UpperCamelCase       )       )
					# verify class_labels
					__lowercase			:  Any								=       torch.tensor([75, 75, 63, 65, 17, 17]       )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,						__UpperCamelCase       )       )
					# verify orig_size
					__lowercase			:  Any								=       torch.tensor([4_80, 6_40]       )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,						__UpperCamelCase       )       )
					# verify size
					__lowercase			:  List[Any]								=       torch.tensor([8_00, 10_66]       )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,						__UpperCamelCase       )       )
		@slow
		def 	_lowerCamelCase							(						self       )						->       int:
					__lowercase			:  Dict								=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
					with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''				,						'''r'''       ) as f:
								__lowercase			:  int								=       json.loads(f.read()       )

					__lowercase			:  Union[str, Any]								=       {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

					__lowercase			:  List[Any]								=       pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''       )

					# encode them
					__lowercase			:  Union[str, Any]								=       DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''       )
					__lowercase			:  List[Any]								=       image_processing(images=__UpperCamelCase				,						annotations=__UpperCamelCase				,						masks_path=__UpperCamelCase				,						return_tensors='''pt'''       )

					# verify pixel values
					__lowercase			:  Tuple								=       torch.Size([1, 3, 8_00, 10_66]       )
					self.assertEqual(encoding['''pixel_values'''].shape				,						__UpperCamelCase       )

					__lowercase			:  str								=       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]       )
					self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,						__UpperCamelCase				,						atol=1E-4       )       )

					# verify area
					__lowercase			:  Optional[Any]								=       torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]       )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,						__UpperCamelCase       )       )
					# verify boxes
					__lowercase			:  List[str]								=       torch.Size([6, 4]       )
					self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,						__UpperCamelCase       )
					__lowercase			:  Optional[Any]								=       torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]       )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,						__UpperCamelCase				,						atol=1E-3       )       )
					# verify image_id
					__lowercase			:  Any								=       torch.tensor([3_97_69]       )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,						__UpperCamelCase       )       )
					# verify is_crowd
					__lowercase			:  Tuple								=       torch.tensor([0, 0, 0, 0, 0, 0]       )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,						__UpperCamelCase       )       )
					# verify class_labels
					__lowercase			:  Tuple								=       torch.tensor([17, 17, 63, 75, 75, 93]       )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,						__UpperCamelCase       )       )
					# verify masks
					__lowercase			:  List[str]								=       82_28_73
					self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()				,						__UpperCamelCase       )
					# verify orig_size
					__lowercase			:  Dict								=       torch.tensor([4_80, 6_40]       )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,						__UpperCamelCase       )       )
					# verify size
					__lowercase			:  List[str]								=       torch.tensor([8_00, 10_66]       )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,						__UpperCamelCase       )       )





"""simple docstring"""



import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	Optional[int]:





    """simple docstring"""

    _UpperCAmelCase        = None
    if token is not None:
        _UpperCAmelCase        = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase        = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
    _UpperCAmelCase        = requests.get(_A       ,   headers=_A   ).json()
    _UpperCAmelCase        = {}

    try:
        job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}   )
        _UpperCAmelCase        = math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0   )

        for i in range(_A   ):
            _UpperCAmelCase        = requests.get(url + F"""&page={i + 2}"""       ,   headers=_A   ).json()
            job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}   )

        return job_links
    except Exception:
        print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""   )

    return {}


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = None
    if token is not None:
        _UpperCAmelCase        = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase        = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
    _UpperCAmelCase        = requests.get(_A       ,   headers=_A   ).json()
    _UpperCAmelCase        = {}

    try:
        artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}   )
        _UpperCAmelCase        = math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0   )

        for i in range(_A   ):
            _UpperCAmelCase        = requests.get(url + F"""&page={i + 2}"""       ,   headers=_A   ).json()
            artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}   )

        return artifacts
    except Exception:
        print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""   )

    return {}


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A   )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = None
    if token is not None:
        _UpperCAmelCase        = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase        = requests.get(_A       ,   headers=_A       ,   allow_redirects=_A   )
    _UpperCAmelCase        = result.headers["""Location"""]
    _UpperCAmelCase        = requests.get(_A       ,   allow_redirects=_A   )
    _UpperCAmelCase        = os.path.join(_A       ,   F"""{artifact_name}.zip"""   )
    with open(_A       ,   """wb"""   ) as fp:
        fp.write(response.content   )


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = []
    _UpperCAmelCase        = []
    _UpperCAmelCase        = None

    with zipfile.ZipFile(_A   ) as z:
        for filename in z.namelist():
            if not os.path.isdir(_A   ):
                # read the file
                if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                    with z.open(_A   ) as f:
                        for line in f:
                            _UpperCAmelCase        = line.decode("""UTF-8"""   ).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    _UpperCAmelCase        = line[: line.index(""": """   )]
                                    _UpperCAmelCase        = line[line.index(""": """   ) + len(""": """   ) :]
                                    errors.append([error_line, error]   )
                                except Exception:
                                    # skip un-related lines
                                    pass
                            elif filename == "summary_short.txt" and line.startswith("""FAILED """   ):
                                # `test` is the test method that failed
                                _UpperCAmelCase        = line[len("""FAILED """   ) :]
                                failed_tests.append(_A   )
                            elif filename == "job_name.txt":
                                _UpperCAmelCase        = line

    if len(_A   ) != len(_A   ):
        raise ValueError(
            F"""`errors` and `failed_tests` should have the same number of elements. Got {len(_A   )} for `errors` """
            F"""and {len(_A   )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
            """ problem."""   )

    _UpperCAmelCase        = None
    if job_name and job_links:
        _UpperCAmelCase        = job_links.get(_A       ,   _A   )

    # A list with elements of the form (line of error, error, failed test)
    _UpperCAmelCase        = [x + [y] + [job_link] for x, y in zip(_A       ,   _A   )]

    return result


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	List[str]:





    """simple docstring"""

    _UpperCAmelCase        = []

    _UpperCAmelCase        = [os.path.join(_A       ,   _A   ) for p in os.listdir(_A   ) if p.endswith(""".zip"""   )]
    for p in paths:
        errors.extend(get_errors_from_single_artifact(_A       ,   job_links=_A   )   )

    return errors


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = Counter()
    counter.update([x[1] for x in logs]   )
    _UpperCAmelCase        = counter.most_common()
    _UpperCAmelCase        = {}
    for error, count in counts:
        if error_filter is None or error not in error_filter:
            _UpperCAmelCase        = {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

    _UpperCAmelCase        = dict(sorted(r.items()       ,   key=lambda _A   : item[1]["count"]       ,   reverse=_A   )   )
    return r


def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = test.split("""::"""   )[0]
    if test.startswith("""tests/models/"""   ):
        _UpperCAmelCase        = test.split("""/"""   )[2]
    else:
        _UpperCAmelCase        = None

    return test


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = [(x[0], x[1], get_model(x[2]   )) for x in logs]
    _UpperCAmelCase        = [x for x in logs if x[2] is not None]
    _UpperCAmelCase        = {x[2] for x in logs}

    _UpperCAmelCase        = {}
    for test in tests:
        _UpperCAmelCase        = Counter()
        # count by errors in `test`
        counter.update([x[1] for x in logs if x[2] == test]   )
        _UpperCAmelCase        = counter.most_common()
        _UpperCAmelCase        = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
        _UpperCAmelCase        = sum(error_counts.values()   )
        if n_errors > 0:
            _UpperCAmelCase        = {"""count""": n_errors, """errors""": error_counts}

    _UpperCAmelCase        = dict(sorted(r.items()       ,   key=lambda _A   : item[1]["count"]       ,   reverse=_A   )   )
    return r


def 			_UpperCamelCase						(    _A   )     ->	Optional[int]:





    """simple docstring"""

    _UpperCAmelCase        = """| no. | error | status |"""
    _UpperCAmelCase        = """|-:|:-|:-|"""
    _UpperCAmelCase        = [header, sep]
    for error in reduced_by_error:
        _UpperCAmelCase        = reduced_by_error[error]["""count"""]
        _UpperCAmelCase        = F"""| {count} | {error[:1_0_0]} |  |"""
        lines.append(_A   )

    return "\n".join(_A   )


def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = """| model | no. of errors | major error | count |"""
    _UpperCAmelCase        = """|-:|-:|-:|-:|"""
    _UpperCAmelCase        = [header, sep]
    for model in reduced_by_model:
        _UpperCAmelCase        = reduced_by_model[model]["""count"""]
        _UpperCAmelCase     ,_UpperCAmelCase        = list(reduced_by_model[model]["""errors"""].items()   )[0]
        _UpperCAmelCase        = F"""| {model} | {count} | {error[:6_0]} | {_count} |"""
        lines.append(_A   )

    return "\n".join(_A   )


if __name__ == "__main__":
 a       :						Optional[Any]       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
 parser.add_argument(
     '''--output_dir''',
     type=str,
     required=True,
     help='''Where to store the downloaded artifacts and other result files.''',
 )
 parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
 a       :						Any       		=		parser.parse_args()

 os.makedirs(args.output_dir, exist_ok=True)

 a       :						Optional[int]       		=		get_job_links(args.workflow_run_id, token=args.token)
 a       :						Optional[int]       		=		{}
 # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
 # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
 if _job_links:
  for k, v in _job_links.items():
   # This is how GitHub actions combine job names.
   if " / " in k:
    a       :						Union[str, Any]       		=		k.find(''' / ''')
    a       :						Optional[int]       		=		k[index + len(''' / ''') :]
   a       :						Optional[int]       		=		v
 with open(os.path.join(args.output_dir, '''job_links.json'''), '''w''', encoding='''UTF-8''') as fp:
  json.dump(job_links, fp, ensure_ascii=False, indent=4)

 a       :						Union[str, Any]       		=		get_artifacts_links(args.workflow_run_id, token=args.token)
 with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
  json.dump(artifacts, fp, ensure_ascii=False, indent=4)

 for idx, (name, url) in enumerate(artifacts.items()):
  download_artifact(name, url, args.output_dir, args.token)
  # Be gentle to GitHub
  time.sleep(1)

 a       :						Any       		=		get_all_errors(args.output_dir, job_links=job_links)

 # `e[1]` is the error
 a       :						int       		=		Counter()
 counter.update([e[1] for e in errors])

 # print the top 30 most common test errors
 a       :						List[str]       		=		counter.most_common(3_0)
 for item in most_common:
  print(item)

 with open(os.path.join(args.output_dir, '''errors.json'''), '''w''', encoding='''UTF-8''') as fp:
  json.dump(errors, fp, ensure_ascii=False, indent=4)

 a       :						List[Any]       		=		reduce_by_error(errors)
 a       :						List[str]       		=		reduce_by_model(errors)

 a       :						Dict       		=		make_github_table(reduced_by_error)
 a       :						int       		=		make_github_table_per_model(reduced_by_model)

 with open(os.path.join(args.output_dir, '''reduced_by_error.txt'''), '''w''', encoding='''UTF-8''') as fp:
  fp.write(sa)
 with open(os.path.join(args.output_dir, '''reduced_by_model.txt'''), '''w''', encoding='''UTF-8''') as fp:
  fp.write(sa)